sf_trees<- read.csv(here("data", "sf_trees", "sf_trees.csv"))
refresh skills for data wrangling and summary statistics using functions in the dplyer package
find top 5 highest obsv of trees by legal_status, do wrangling, make a graph using: count
top_five_status <- sf_trees %>%
count(legal_status) %>% #count does summarize, n function, and group by in one fucntion
drop_na(legal_status) %>% # for any variable, just need to specify can be multiple
rename(tree_count = n) %>% #NEW NAME GOES ON THE LEFT
relocate(tree_count) %>% #single column will move it to the front; must use new name bc rename came first
slice_max(tree_count, n = 5) #identify rows with high values and only keeps top ones, there is also a min
Make a graph by top five observations by legal obsv
ggplot(top_five_status, aes(x = fct_reorder(legal_status, tree_count), y = tree_count))+ #change to factor associated with tree count associated with that
geom_col()+
labs(
x = "Legal Status",
y = "Tree Count"
)+
coord_flip()+
theme_minimal()
only want to keep obsv (rows) blackwood acacia trees
blackwood_acacia <-sf_trees %>%
filter(str_detect(species, "Blackwood Acacia")) %>% #keep observations wehre a certain string is detected ANYWHERE within that variable
select(legal_status, date, latitude, longitude)
ggplot(blackwood_acacia, aes(x = longitude, y = latitude))+
geom_point()
## Warning: Removed 27 rows containing missing values (geom_point).
usefull for combining and separating columns
common name and scientific name, split them using the :: in the dataset!
sf_trees_sep <- sf_trees %>%
separate(species, into = c("spp_scientific", "spp_common"), sep = "::")
example of tidyr unite
combine tree and legal status into same column. probaby wouldnt do this
sf_trees_unite <- sf_trees %>%
unite("id_status", tree_id:legal_status, sep = "_cool_")
use st_as_sf() to convert the lat and long to spatial coordinates set a coord ref system
blackwood_acacia_sp <- blackwood_acacia %>%
drop_na(latitude, longitude) %>%
st_as_sf(coords = c("longitude", "latitude"))
st_crs(blackwood_acacia_sp)= 4326
ggplot(blackwood_acacia_sp)+
geom_sf(color = "darkgreen")
read in sf roads shp file
sf_map <-read_sf(here("data", "sf_map", "tl_2017_06075_roads.shp")) #read spatial data so use sf
st_transform(sf_map, 4326)
## Simple feature collection with 4087 features and 4 fields
## geometry type: LINESTRING
## dimension: XY
## bbox: xmin: -122.5136 ymin: 37.70813 xmax: -122.3496 ymax: 37.83213
## geographic CRS: WGS 84
## # A tibble: 4,087 x 5
## LINEARID FULLNAME RTTYP MTFCC geometry
## * <chr> <chr> <chr> <chr> <LINESTRING [°]>
## 1 110498938… Hwy 101 S O… M S1400 (-122.4041 37.74842, -122.404 37.7483, -…
## 2 110498937… Hwy 101 N o… M S1400 (-122.4744 37.80691, -122.4746 37.80684,…
## 3 110366022… Ludlow Aly … M S1780 (-122.4596 37.73853, -122.4596 37.73845,…
## 4 110608181… Mission Bay… M S1400 (-122.3946 37.77082, -122.3929 37.77092,…
## 5 110366689… 25th Ave N M S1400 (-122.4858 37.78953, -122.4855 37.78935,…
## 6 110368970… Willard N M S1400 (-122.457 37.77817, -122.457 37.77812, -…
## 7 110368970… 25th Ave N M S1400 (-122.4858 37.78953, -122.4858 37.78952,…
## 8 110498933… Avenue N M S1400 (-122.3643 37.81947, -122.3638 37.82064,…
## 9 110368970… 25th Ave N M S1400 (-122.4854 37.78983, -122.4858 37.78953)
## 10 110367749… Mission Bay… M S1400 (-122.3865 37.77086, -122.3878 37.77076,…
## # … with 4,077 more rows
ggplot(sf_map)+
geom_sf()
use st_transform(sf map) to get the crs format!! dont want to mix crs
combine blackwood acacia tree obsv and sf roads map:
ggplot()+ #but nothing within it bc want to specify the data for each geom within the sf
geom_sf(data = sf_map, size = 0.1, color = "darkgray")+
geom_sf(data = blackwood_acacia_sp, color = "red", size = 0.5)+
theme_void()
tmap_mode("view")
## tmap mode set to interactive viewing
tm_shape(blackwood_acacia_sp) + # base layer
tm_dots()